Prevention of Wildfires Using an AI-Based Open Conductor Fault Detection Method on Overhead Line

Junsoo Che, Taehun Kim, Suhan Pyo, Jaedeok Park, Byeonghyeon An and Taesik Park ()
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Junsoo Che: Department of Electrical and Control Engineering, Mokpo National University, Muan 58554, Republic of Korea
Taehun Kim: Department of Electrical and Control Engineering, Mokpo National University, Muan 58554, Republic of Korea
Suhan Pyo: Department of Electrical and Control Engineering, Mokpo National University, Muan 58554, Republic of Korea
Jaedeok Park: Department of Electrical and Control Engineering, Mokpo National University, Muan 58554, Republic of Korea
Byeonghyeon An: Department of Electrical and Control Engineering, Mokpo National University, Muan 58554, Republic of Korea
Taesik Park: Department of Electrical and Control Engineering, Mokpo National University, Muan 58554, Republic of Korea

Energies, 2023, vol. 16, issue 5, 1-24

Abstract: Overhead lines that are exposed to the outdoors are susceptible to faults such as open conductors on weak points and disconnection caused by external factors such as typhoons. Arcs that occur during disconnection generate energy at a high heat of over 10,000 °C, requiring swift fault shut-off. However, most conventional fault detection methods to protect electrical power systems detect an overcurrent; thus, they can only detect faults after the line is disconnected and the cross-section of the line that generates the arc discharge makes contact with another line or the ground, causing a high risk of fire. Furthermore, in the case of ground faults owing to the disconnection of overhead lines, the load and the grounding impedance are not parallel. Therefore, in the case of the fault current not exceeding the threshold or a high impedance fault due to the high grounding impedance of the surrounding environment, such as grass or trees, it is difficult to determine overhead line faults with conventional fault detection methods. To solve these issues, this paper proposes an AI-based open conductor fault detection method on overhead lines that can clear the fault before the falling open conductor line comes into contact with the ground’s surface so as to prevent fire. The falling time according to the height and span of the overhead line was calculated using a falling conductor model for the overhead line, to which the pendulum motion was applied. The optimal input data cycle that enables fault detection before a line–ground fault occurs was derived. For artificial intelligence learning to prevent wildfires, the voltage and current signals were collected through a total of 432 fault simulations and were wavelet-transformed with a deep neural network to verify the method. The proposed total scheme was simulated and verified with MATLAB.

Keywords: open conductor fault; deep neural network; fire protection; fault detection; high impedance fault (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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